@inproceedings{bccd2f7686c04d289eee8f93ac3da39f,
title = "Compressing Inside Generating: A Latent Domain Codec for AI-Generated Images",
abstract = "Latent diffusion models (LDMs) have emerged as a prominent framework for image generation, consisting of a diffusion model \$\textbackslash{}mathcal\{M\}\$ and a VAE decoder \$\textbackslash{}mathcal\{D\}\$. High-quality image generation models are large and computationally intensive. As a result, image generation is typically performed on cloud servers, with the generated images then transmitted to edge devices.",
author = "Yuxu Chen and Zhenhao Sun and Yuliang Huang and Lei Deng and Wei Han and Bo Bai and Shiqi Wang",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 2025 Data Compression Conference, DCC 2025 ; Conference date: 18-03-2025 Through 21-03-2025",
year = "2025",
doi = "10.1109/DCC62719.2025.00051",
language = "英语",
series = "Data Compression Conference Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "363",
editor = "Ali Bilgin and Fowler, \{James E.\} and Joan Serra-Sagrista and Yan Ye and Storer, \{James A.\}",
booktitle = "Proceedings - DCC 2025",
address = "美国",
}